Nowadays, wireless communications have become increasingly popular and wireless networks show a continuous increasing transmission capacity with the general use of more powerful modulation techniques, such as M-ary Quadrature Amplitude Modulation (QAM).
QAM provides a constellation of a M number of modulation values (each having a different combination of phase and amplitude), wherein each constellation point (symbol) represents a plurality of information bits. The number of bits that are represented by each symbol in a M-ary QAM system is equal to log2M. Different M-ary QAM constellations are widely spread, from the robust 4QAM, to the high rate 64QAM constellation.
QAM technique can be advantageously combined with more recent schemes such as the Orthogonal Frequency Division Multiplex (OFDM) as well as the Multiple In Multiple Out (MIMO) techniques.
In a MIMO system, comprising M transmit antennas and N receive antennas, the receiver has to process a set of M transmitted symbols (for instance in OFDM) from a set of N observed signals, which signals might be corrupted by the non-ideal characteristics of the channel and noise. The detector's role is to choose one s among all possible transmitted symbol vectors based on the received data, and the estimated channel. As known by the skilled man, the detector which always return the optimal solution is the so-called Maximum Likelihood (ML) detector, the implementation of which shows to be of prohibitive complexity.
However, such optimal Maximum Likelihood detector can be efficiently approximated by the use of several techniques such as Sphere Decoding, Lattice Reduction or a combination of both, often referred as soft-decision near-ML techniques. The soft-decision near-ML provides exact Log-Likelihood-Ratio (LLR) computation for most of the bits constituting the channel and MIMO encoded is transmitted sequence.
Generally speaking, as basically illustrated in FIG. 1, the near-ML detector generates a list of distances for a given bit to be used in a second step for the computation of the LLR.
In the case of max-log approximation, the above mentioned list of distances is approximated by only two distances computed between the point of the received demodulated symbol and a hypothetic constellation symbol having respective bit values of 1 and 0.
The LLR estimate for a k−th bit can be computed in accordance with the following formula, well known to a skilled man:LLRk=1/σ2*(d21min,k−d20min,k)with d21min,k being the minimum distance between the received demodulated symbol and a QAM constellation point where that particular bit equals one and d20min,k being the minimum distance between the received demodulated symbol and a QAM constellation point where that particular bit equals zero. Also, σ2 denotes the noise variance.
In order to reduce its complexity (with respect to the ML detector), the near-ML detector performs the computation of the LLR only for a limited number of bits belonging to the sequence.
For other bits belonging to the sequence, the LLR is not explicitly calculated and it is generally set to a predefined value. This operation is commonly referred as LLR clipping, as addressed in the following two references:
[1] B. M. Hochwald and S. ten Brink. “Achieving near-capacity on a multiple-antenna channel”. Communications, IEEE Transactions on, vol. 51, p. 389-399, March 2003.
[2] Y. de Jong and T. Willink, “Iterative Tree Search Detection for MIMO wireless systems”, Communications, IEEE Transactions on, vol. 53, p. 930-935, June 2005.
However, the choice of the clipping level has a strong impact on the system performance.
Generally speaking, if the chosen clipping level is too high, this prevents the correction of decision errors occurring at some bit positions, resulting in poor performance. Conversely, if the chosen clipping level is too low, this limits the mutual information at the detector output and also leads to decreased performance.
The article “Channel State Information Based LLR Clipping in List MIMO Detection” by David L. Milliner et. al., School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Ga. 30332-0250 discloses an improved mechanism which uses clipping levels based on the knowledge of the SNR (Signal to Noise Ratio). These results are compared to the [2] reference that uses Fixed LLR Clipping Levels (FLC) set to +3 and −3 according to the bit sign. Although this prior art has been shown to be a significant improvement with respect to the FLC, such technique relies on the presence of a Gaussian signal which does not correspond to the reality of the discrete QAM constellation.
There is therefore a wish for a more appropriate mechanism.